Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach

Fu, Q; Luo, Y; Wen, Y; Tao, D; Li, Y; Duan, LY

Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach

Fu, Q Luo, Y Wen, Y Tao, D

Li, Y Duan, LY

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2018, 20 (8), pp. 2114 - 2125
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Fu, Q	en_US
dc.contributor.author	Luo, Y	en_US
dc.contributor.author	Wen, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Duan, LY	en_US
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	IEEE Transactions on Multimedia, 2018, 20 (8), pp. 2114 - 2125	en_US
dc.identifier.issn	1520-9210	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131733
dc.description.abstract	© 1999-2012 IEEE. It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In the system, a key component is high-precision product search, which is to fulfill exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modeled and exploited by a simple Euclidean distance. This motivates us to introduce distance metric learning (DML) for improving the distance estimation. However, in traditional DML methods, the side information (such as the similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain is leveraged. These methods may fail due to limited side information. Fortunately, this issue can be alleviated by utilizing transfer metric learning (TML) to exploit information from other related domains. In this paper, a novel manifold regularized heterogeneous multitask metric learning framework is proposed, in which each domain is treated equally. The proposed approach allows us to simultaneously exploit the information from other domains and the unlabeled information. Furthermore, the ranking-based loss is adopted to make our model more appropriate for search. Experiments on two challenging real-world datasets demonstrate the effectiveness of the proposed method. This TML approach is expected to impact the transformation of the emerging T2O trend in both TV and online video domains.	en_US
dc.relation.ispartof	IEEE Transactions on Multimedia	en_US
dc.relation.isbasedon	10.1109/TMM.2018.2791803	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	20	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 1999-2012 IEEE. It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In the system, a key component is high-precision product search, which is to fulfill exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modeled and exploited by a simple Euclidean distance. This motivates us to introduce distance metric learning (DML) for improving the distance estimation. However, in traditional DML methods, the side information (such as the similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain is leveraged. These methods may fail due to limited side information. Fortunately, this issue can be alleviated by utilizing transfer metric learning (TML) to exploit information from other related domains. In this paper, a novel manifold regularized heterogeneous multitask metric learning framework is proposed, in which each domain is treated equally. The proposed approach allows us to simultaneously exploit the information from other domains and the unlabeled information. Furthermore, the ranking-based loss is adopted to make our model more appropriate for search. Experiments on two challenging real-world datasets demonstrate the effectiveness of the proposed method. This TML approach is expected to impact the transformation of the emerging T2O trend in both TV and online video domains.

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http://hdl.handle.net/10453/131733